Jun 2024 – Oct 2024 · 4 mos

Mar 2022 – May 2024 · 2 yrs 2 mos

• Collaborated closely with Product Managers, UI engineers, backend leads, and developers across multiple Business Units within the Fulfilment Supply Chain Group (FSG) to drive cross-functional initiatives.
• Designed high-level architecture spanning multiple systems and teams, enabling scalable and cohesive product development.
• Proactively identified product gaps and delivered reliable, maintainable solutions aligned with long-term goals.
• Owned code quality and technical hygiene: conducted code reviews, optimized performance bottlenecks, resolved security vulnerabilities, and created playbooks for recurring issues in legacy systems.
• Spearheaded multiple initiatives to address platform-level pain points and inefficiencies; conducted Proof of Concepts (PoCs) for proposed solutions.
• Several of these initiatives were successfully adopted by multiple teams, driving improvements in developer productivity and system stability.

Jan 2019 – Mar 2022 · 3 yrs 2 mos

• Played a key role in the Kirana Scaling Project, stabilizing and optimizing the supply chain flows of Flipkart’s Kirana-based delivery model.
• Contributed to the supply chain backend of Flipkart’s hyperlocal delivery initiative, enabling faster last-mile fulfillment.
• Led CI/CD pipeline setup and successfully migrated services to Kubernetes, improving deployment velocity and infrastructure resilience.
• Scaled critical backend systems in preparation for Flipkart’s Big Billion Day sale, ensuring high availability and performance under peak load.
• Recognitions:
• Spot Award for Q2 (April–June), 2019
• Spot Award for Kirana Scaling (Q4, Oct–Dec), 2019
• Monthly Spot Award – July 2021

Jun 2015 – Dec 2018 · 3 yrs 6 mos

• As part of the core data team in my first role, I contributed to building distributed systems, real-time pipelines, and large-scale data infrastructure from the ground up. Key projects included:
• || Distributed Cache System
• Developed a Python-based consistent hashing library (Ketama ring strategy) for distributing user data across multiple Redis servers, improving scalability and fault tolerance.
• || DMP User Data Ingestion Pipeline
• Built a standalone ingestion tool to process and ingest raw user data from multiple Data Management Platforms into Redis:
• Java processor to handle large file batches
• AWS Lambda + SQS integration for real-time file handling
• Custom bit-level compression algorithm to optimize storage of integer-heavy data
• || Apache Spark Applications
• Designed and deployed several Spark-based data processing systems:
• Data ingestion framework for pulling data from S3, Redis, and SQL based on user application type
• Graph-based User Journey tracking system
• Campaign Impression Analysis tool
• Redis cluster rebalancing utility
• Custom Auto Scaling mechanism for Spark workers based on workload characteristics
• || Real-Time Conversion Tracking
• Engineered a low-latency tracking system to determine ad conversion impact — tracking user behavior post-impression to assess campaign effectiveness.
• || CloudWatch Logs Monitor
• Wrote an AWS Lambda function to monitor access logs from EC2 instances, raising alarms when error thresholds were breached — improving system reliability.
• || Data Warehousing with Redshift
• Set up and optimized Amazon Redshift clusters for analytics use cases involving large datasets and complex aggregations.
• || Recognition
• Awarded Most Valued Player of the data team for outstanding contributions during 2015–2016.